1. fMRI data analysis at CCBI
Vladimir Cherkassky

2. Detection of Activated Voxels
Always performed on a voxel-to-voxel basis
T-test comparing mean values during two experimental conditions (FIASCO)
Covariance of the time course with the paradigm function (SPM, Voxcor.id, etc.)
Threshold estimation and selection

3. FIASCO data preprocessing
Baseline correction
de-ghosting
mean correction
motion correction
outlier detection and removal
Trend correction
T-map computation

4. Processing steps (single subject)
Fiasco processing (active voxel detection)
Co-registration of functional and anatomical data.
Subject-specific ROI definition
Volume and distribution of activation
Functional connectivity
Morphing into standard space

5. Activated voxels detected at t=6

6. Probability map thresholding
Correction for multiple comparisons
Scanner-specific data properties
Spatial correlation among voxels
Additional considerations
Comparing groups:
“High activators” and “low activators”
Normalization (selecting most activated voxels)
FDR (false discovery rate) method

7. Co-registration of functional and anatomical data

8. Brain areas

9. Regions Of Inerest (ROI)

10. Activation at t=6 and ROIs defined on the basis of subject-specific anatomical landmarks
Number of activated voxels per ROI

11. Processing steps (group analysis)
ANOVA analysis of volume of activation
ANOVA analysis of location of activation (centroids in standard space)
ANOVA analysis of functional connectivity
Standard space averaging (“hit” maps)
Additional analyses:
Factor analysis, MDS, ...

12. Average volume of activation per ROI for the two groups of subjects:
autistic participants and matched control participants.
Sentence comprehension task.
Highlighted areas show statistically significant group difference.
Normalization thresholds used for between-group analysis.

13. Difference in the distribution of activation in the main language areas for the two groups

14. Averaged group activation

15. Overall pattern of activation: similar pattern for three groups of subjects

16. Functional connectivity
FC is measured as a correlation between averaged time courses (tc) of activated voxels for the two brain areas (ROIs)
Synchronization can be induced by the connection (direct or indirect) between areas or some common input.
We interpret FC as a measure of interaction between brain areas working together on the same task.

17. Time course of activated voxels
Useful signal is ~2%
As a result, single voxel tc is extremely noisy
Averaged tc of all activated voxels within an ROI (min number of voxels we use is 3)
Most of the ROIs consist of more than one slice
Correction for the slice acquisition sequence is necessary for proper connectivity estimation
Images to include in time course calculation

18. two areas involved in the language task.
Averaged tc for the
two areas involved in the
language task.
Note the scale (% signal
change from fixation) and
the images included in the
analysis.

19. Averaged tc with different
methods of interpolation
(correction for slice
acquisition time)
Though the differences look
minor, linear interpolation
introduces considerably larger
level of correlation between
the time courses.

20. Between subjects variability of the averaged tc (event-related study)

21. Functional connectivity within the
language network for autistic and
control groups.
Note the systematically higher
connectivity level for the control
group.

22. Pairs of areas with statistically
significant differences in
functional connectivity for the
two groups of participants

23. Overall connectivity
pattern:
1. Note the high level
of similarity for the two
groups (r=0.69 for all
measured connections;
r=0.95 for the connections
with significant
differences)
2. Note the systematically
higher connectivity for the
control group

24. Conclusions
Our approach provides accurate measures of activation volume and location, as well as functional connectivity between the brain areas.
These measures can be used for testing the effects of experimental manipulations for single subject, group, and between-group analyses.